AI for Postgres Training Course

Introduction to AI within Postgres

• Overview of AI and data-centric systems.
• Exploring AI use cases in Postgres environments.
• Architectural considerations for AI workloads.

Environment Setup

• Installing PostgreSQL and configuring pgvector.
• Preparing Python for AI integrations.
• Linking Postgres with local and cloud-based LLMs.

AI Extensions and Vector Databases

• Comprehending vector embeddings in Postgres.
• Applying pgvector for similarity search and semantic queries.
• Benchmarking AI extensions against external vector stores.

LLM Integration with Postgres

• Connecting Postgres with OpenAI, Deepseek, Qwen, and Mistral Small.
• Designing efficient AI query pipelines.
• Optimizing the storage and retrieval of embeddings.

Creating Intelligent Query Systems

• Translating natural language to SQL using LLMs.
• Automating query generation and optimization processes.
• Implementing AI-assisted database search and summarization.

Optimizing Postgres for AI Workloads

• Developing indexing strategies for embeddings.
• Performance tuning and caching for AI-related queries.
• Scaling Postgres using distributed and cloud architectures.

Security and Governance in AI-Enabled Databases

• Addressing data privacy and compliance requirements.
• Managing API keys and access controls.
• Auditing AI interactions and query logs.

Case Studies and Enterprise Applications

• Developing AI-powered recommendation systems using Postgres.
• Enhancing enterprise search and analytics with embeddings.
• Implementing automation and predictive modeling within Postgres.

Summary and Future Directions